
import pandas as pd
import plotly as plt
import numpy as np

pd.options.display.max_rows = 4000

import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')

import plotly.express as px 
import plotly.subplots as tls
import plotly
import plotly.offline as py
from plotly.offline import init_notebook_mode, iplot, plot
import plotly.graph_objs as go
init_notebook_mode(connected=True)

from pandas_profiling import ProfileReport

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split, KFold, RandomizedSearchCV, GridSearchCV, StratifiedShuffleSplit
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

SEED = 42


df_final = pd.read_csv('out2.zip')


df_final = df_final.drop(['goal', 'pledged', 'usd pledged'], axis = 1)


df_final.describe(include='all', datetime_is_numeric=True)


# Converting the columns into the right dtypes as for dates and numbers.
df_final["deadline"] = pd.to_datetime(df_final['deadline'])
df_final["launched"] = pd.to_datetime(df_final['launched'])
df_final["ID"] = pd.to_numeric(df_final["ID"])
df_final["backers"] = pd.to_numeric(df_final["backers"])
df_final["real_usd_pledged"] = pd.to_numeric(df_final["real_usd_pledged"])
df_final["usd_goal"] = pd.to_numeric(df_final["usd_goal"])


df_final.dtypes

ID                           int64
name                        object
category                    object
main_category               object
currency                    object
deadline            datetime64[ns]
launched            datetime64[ns]
state                       object
backers                      int64
country                     object
real_usd_pledged           float64
usd_goal                   float64
dtype: object


df_final.isnull().any()

ID                  False
name                False
category            False
main_category       False
currency            False
deadline            False
launched            False
state               False
backers             False
country              True
real_usd_pledged    False
usd_goal            False
dtype: bool


df_final[df_final['country'].isnull()].head()


df_final[df_final['country'].isnull()].shape

(210, 12)


df_final = df_final[~df_final['country'].isnull()]


df_final = df_final.loc[~((df_final['real_usd_pledged']>=df_final['usd_goal']) & (df_final['state']=='failed'))]


df_final = df_final.reset_index(drop=True)


df_final.isnull().any()

ID                  False
name                False
category            False
main_category       False
currency            False
deadline            False
launched            False
state               False
backers             False
country             False
real_usd_pledged    False
usd_goal            False
dtype: bool


df_final.shape

(281637, 12)


df_final.duplicated().sum()

0


counts = df_final['name'].value_counts().rename_axis('name').reset_index(name='counts')


duplicate_names = df_final[df_final['name'].isin(counts[counts['counts']>1].name.tolist())]


duplicate_names.shape

(3022, 12)


duplicate_names.sort_values(by=['name']).head()


def plot_continuous_vars(data, column_name):
    plot_dims = (14, 8)
    fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=False, figsize=plot_dims)
    sns.distplot(data[column_name], ax=ax1)
    sns.distplot(np.log1p(data[column_name]), ax=ax2)


plot_continuous_vars(df_final, 'usd_goal')

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).


plot_continuous_vars(df_final, 'real_usd_pledged')

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).


df_failed = df_final[df_final["state"] == "failed"]
df_sucess = df_final[df_final["state"] == "successful"]


# Add histogram data
failed = np.log(df_failed['usd_goal']+1)
success = np.log(df_sucess['usd_goal']+1)

trace1 = go.Histogram(
    x=failed,
    opacity=0.60, nbinsx=30, name='Goals Failed', histnorm='probability'
)
trace2 = go.Histogram(
    x=success,
    opacity=0.60, nbinsx=30, name='Goals Sucessful', histnorm='probability'
)

data = [trace1, trace2]
layout = go.Layout(barmode='overlay', title=go.layout.Title(text="Distributions of usd_goal"))

fig = go.Figure(
    data=data,
    layout=layout
)

iplot(fig)


import plotly.express as px
fig = px.box(df_final, x="main_category", y="usd_goal")
fig.show()


df_failed = df_final[df_final["state"] == "failed"]
df_success = df_final[df_final["state"] == "successful"]

plot_continuous_vars(df_failed, 'backers')
plot_continuous_vars(df_success, 'backers')

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

C:\Users\ymaricar\anaconda3\lib\site-packages\seaborn\distributions.py:2551: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).


def getDelta(a,b):
    '''Get diffence in days between launch and deadline'''
    return (a - b).days

# Duration of the project   
df_final['duration'] = df_final.apply(lambda x: getDelta(x['deadline'],x['launched']),axis = 1)


df_final['month'] = df_final['launched'].dt.month
df_final['year_month'] = df_final['launched'].map(lambda x: str(x.year) + "-" + str(x.month))


import re

def has_non_chars(name):
    for c in name:
        if not c.isalpha() and c!='?' and c!='!':
            return 1
    return 0

def has_exclamation_interrogation(name):
    if ("!" in name or "?" in name):
        return 1
    return 0

def has_upper(name):
    for word in name.split(' '):
        if word.isupper() and len(re.sub(r'\W+', '', word))>1:
            return 1
    return 0


df_final['len_name'] = df_final.name.str.len()


df_final['name_nb_words'] = df_final.name.apply(lambda x: len(str(x).split(' ')))


df_final['name_non_chars'] = df_final.name.apply(has_non_chars)


df_final['name_has_symbol'] = df_final.name.apply(has_exclamation_interrogation)


df_final['name_upper'] = df_final.name.apply(has_upper)


df_final['cat_full'] = df_final[["main_category","category"]].agg('-'.join, axis=1)


df_final.head()


df_final.columns

Index(['ID', 'name', 'category', 'main_category', 'currency', 'deadline',
       'launched', 'state', 'backers', 'country', 'real_usd_pledged',
       'usd_goal', 'duration', 'month', 'year_month', 'len_name',
       'name_nb_words', 'name_non_chars', 'name_has_symbol', 'name_upper',
       'cat_full'],
      dtype='object')


ks = df_final.drop(['ID','name','deadline','launched','year_month', 'backers', 'real_usd_pledged'], axis=1).copy()


ks.columns

Index(['category', 'main_category', 'currency', 'state', 'country', 'usd_goal',
       'duration', 'month', 'len_name', 'name_nb_words', 'name_non_chars',
       'name_has_symbol', 'name_upper', 'cat_full'],
      dtype='object')


ks['usd_goal_corrected'] = np.log1p(ks['usd_goal'])


ks['state'] = ks.state.map(dict(successful=1, failed=0))


profile = ProfileReport(ks, title="Pandas Profiling Report Kickstarter")
profile.to_file('kickstarterds.html')


# ## This heatmap is also available from pandas-profiling html file.
# corr = ks.corr()
# dims = (16, 10)
# fig, ax = plt.subplots(figsize = dims)
# sns.heatmap(corr, 
#             xticklabels=corr.columns.values,
#             yticklabels=corr.columns.values,ax = ax, cmap="Blues")


# We'll drop name_nb_words because it's highly correlated with len_name
ks = ks.drop(['name_nb_words'], axis=1)
# We can drop currency too as the currency is explained by the country
ks = ks.drop(['currency'], axis=1)
# We can drop category and main_category as it's encoded in cat_full
ks = ks.drop(['category','main_category'], axis=1)


ks.columns

Index(['state', 'country', 'usd_goal', 'duration', 'month', 'len_name',
       'name_non_chars', 'name_has_symbol', 'name_upper', 'cat_full',
       'usd_goal_corrected'],
      dtype='object')


ks.state.value_counts(normalize=True)

0    0.598267
1    0.401733
Name: state, dtype: float64


ks.dtypes

state                   int64
country                object
usd_goal              float64
duration                int64
month                   int64
len_name                int64
name_non_chars          int64
name_has_symbol         int64
name_upper              int64
cat_full               object
usd_goal_corrected    float64
dtype: object


ks.describe(include='all')


y = ks.state
x = ks.drop(['state','usd_goal'], axis = 1)


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=SEED)


print('x_train.shape:', x_train.shape)
print('y_train.shape:', y_train.shape)
print('x_test.shape :', x_test.shape)
print('y_test.shape :', y_test.shape)

x_train.shape: (225309, 9)
y_train.shape: (225309,)
x_test.shape : (56328, 9)
y_test.shape : (56328,)


x_train.columns

Index(['country', 'duration', 'month', 'len_name', 'name_non_chars',
       'name_has_symbol', 'name_upper', 'cat_full', 'usd_goal_corrected'],
      dtype='object')


from pprint import pprint
import mlflow

def fetch_logged_data(run_id):
    client = mlflow.tracking.MlflowClient()
    data = client.get_run(run_id).data
    tags = {k: v for k, v in data.tags.items() if not k.startswith("mlflow.")}
    artifacts = [f.path for f in client.list_artifacts(run_id, "model")]
    return data.params, data.metrics, tags, artifacts

# enable autologging
mlflow.sklearn.autolog()


from sklearn.dummy import DummyClassifier
# define model

model = DummyClassifier(strategy='uniform', random_state=42)
with mlflow.start_run() as run:
    model.fit(x_train, y_train)
    

# fetch logged data
params, metrics, tags, artifacts = fetch_logged_data(run.info.run_id)

pprint(params)
pprint(metrics)
pprint(tags)
pprint(artifacts)

2021/06/17 06:44:25 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."

{'constant': 'None', 'random_state': '42', 'strategy': 'uniform'}
{'training_accuracy_score': 0.500264081772144,
 'training_f1_score': 0.5051236324432034,
 'training_log_loss': 0.6931471805599451,
 'training_precision_score': 0.5195800439022963,
 'training_recall_score': 0.500264081772144,
 'training_roc_auc_score': 0.5,
 'training_score': 0.500264081772144}
{'estimator_class': 'sklearn.dummy.DummyClassifier',
 'estimator_name': 'DummyClassifier'}
['model/MLmodel', 'model/conda.yaml', 'model/model.pkl']


x_train.columns

Index(['country', 'duration', 'month', 'len_name', 'name_non_chars',
       'name_has_symbol', 'name_upper', 'cat_full', 'usd_goal_corrected'],
      dtype='object')


from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import StandardScaler, RobustScaler, OneHotEncoder

numeric_features = ['usd_goal_corrected', 'duration', 'len_name']

numeric_transformer = Pipeline(
    steps=[
        ("imputer", SimpleImputer(strategy="mean")),
        ("scaler", RobustScaler()),
    ]
)

categorical_features = ['country', 'cat_full', 'month', 'name_non_chars', 'name_has_symbol', 'name_upper']
categorical_transformer = Pipeline(
    steps=[("onehot", OneHotEncoder(handle_unknown="ignore"))]
)

preprocessing = ColumnTransformer(
    transformers=[
        ("num", numeric_transformer, numeric_features),
        ("cat", categorical_transformer, categorical_features),
    ],
    remainder="passthrough",
)


def predict_plot(X, y, classifier, classifier_name):
#     # predict probabilities
#     lr_probs = classifier.predict_proba(X)
#     # keep probabilities for the positive outcome only
#     lr_probs = lr_probs[:, 1]
#     # predict class values
#     yhat = classifier.predict(X)
#     precision = precision_score(y, yhat)
#     lr_precision, lr_recall, _ = precision_recall_curve(y, lr_probs)
#     lr_f1, lr_auc = f1_score(y, yhat), auc(lr_recall, lr_precision)
#     # summarize scores
#     print(classifier_name+': precision=%.3f auc=%.3f' % (precision, lr_auc))
#     # plot the precision-recall curves
#     no_skill = len(y[y==1]) / len(y)
#     pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
#     pyplot.plot(lr_recall, lr_precision, marker='.', label=classifier_name)
#     # axis labels
#     pyplot.xlabel('Recall')
#     pyplot.ylabel('Precision')
#     # show the legend
#     pyplot.legend()
#     # show the plot
#     pyplot.show()

    yhat = classifier.predict(X)
    
    # Compute fpr, tpr, thresholds and roc auc
    fpr, tpr, thresholds = roc_curve(y, yhat)
    roc_auc = roc_auc_score(y, yhat)

    # Plot ROC curve
    plt.plot(fpr, tpr, label='ROC curve (area = %0.3f)' % roc_auc)
    plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.0])
    plt.xlabel('False Positive Rate or (1 - Specifity)')
    plt.ylabel('True Positive Rate or (Sensitivity)')
    plt.title('Receiver Operating Characteristic')
    plt.legend(loc="lower right")


# precision-recall curve and f1 for evaluation purposes

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve, roc_curve
from sklearn.metrics import f1_score, roc_auc_score
from sklearn.metrics import auc
from matplotlib import pyplot

lr = LogisticRegression(solver='liblinear')
# model = LogisticRegression(solver='lbfgs')
model = Pipeline([('preprocessing', preprocessing),
                ('lr',lr)])
model.fit(x_train, y_train)

predict_plot(x_train, y_train, model, "Logistic Regression")

2021/06/17 06:46:31 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '8b0fe6f58a264041b86e8d535d7777ac', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current sklearn workflow
2021/06/17 06:46:31 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:46:31 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:46:31 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:46:37 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."


predict_plot(x_test, y_test, model, "Logistic Regression")


from lightgbm import LGBMClassifier

# #Specifying the parameter
# params={}
# params['learning_rate']=0.03
# params['boosting_type']='gbdt' #GradientBoostingDecisionTree
# params['objective']='binary' #Binary target feature
# params['metric']='binary_logloss' #metric for binary classification
# params['max_depth']=10
# #train the model 
# clf=lgb.train(params,d_train,100) #train the model on 100 epochs
# #prediction on the test set
# y_pred=clf.predict(X_test)

clf = make_pipeline(
    preprocessing,
    LGBMClassifier()
)

clf.fit(x_train, y_train)
    
predict_plot(x_train, y_train, clf, 'GBM')

2021/06/17 06:46:42 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID 'd5f5e1721fa5494980344f3f8f7ab27c', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current sklearn workflow
2021/06/17 06:46:42 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('columntransformer', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Ro...`
2021/06/17 06:46:42 WARNING mlflow.utils: Truncated the value of the key `columntransformer`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:46:42 WARNING mlflow.utils: Truncated the value of the key `columntransformer__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:46:49 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."


predict_plot(x_test, y_test, clf, 'GBM')


make_pipeline(preprocessing, clf).get_params()

{'memory': None,
 'steps': [('columntransformer',
   ColumnTransformer(remainder='passthrough',
                     transformers=[('num',
                                    Pipeline(steps=[('imputer', SimpleImputer()),
                                                    ('scaler', RobustScaler())]),
                                    ['usd_goal_corrected', 'duration',
                                     'len_name']),
                                   ('cat',
                                    Pipeline(steps=[('onehot',
                                                     OneHotEncoder(handle_unknown='ignore'))]),
                                    ['country', 'cat_full', 'month',
                                     'name_non_chars', 'name_has_symbol',
                                     'name_upper'])])),
  ('pipeline',
   Pipeline(steps=[('columntransformer',
                    ColumnTransformer(remainder='passthrough',
                                      transformers=[('num',
                                                     Pipeline(steps=[('imputer',
                                                                      SimpleImputer()),
                                                                     ('scaler',
                                                                      RobustScaler())]),
                                                     ['usd_goal_corrected',
                                                      'duration', 'len_name']),
                                                    ('cat',
                                                     Pipeline(steps=[('onehot',
                                                                      OneHotEncoder(handle_unknown='ignore'))]),
                                                     ['country', 'cat_full',
                                                      'month', 'name_non_chars',
                                                      'name_has_symbol',
                                                      'name_upper'])])),
                   ('lgbmclassifier', LGBMClassifier())]))],
 'verbose': False,
 'columntransformer': ColumnTransformer(remainder='passthrough',
                   transformers=[('num',
                                  Pipeline(steps=[('imputer', SimpleImputer()),
                                                  ('scaler', RobustScaler())]),
                                  ['usd_goal_corrected', 'duration',
                                   'len_name']),
                                 ('cat',
                                  Pipeline(steps=[('onehot',
                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                  ['country', 'cat_full', 'month',
                                   'name_non_chars', 'name_has_symbol',
                                   'name_upper'])]),
 'pipeline': Pipeline(steps=[('columntransformer',
                  ColumnTransformer(remainder='passthrough',
                                    transformers=[('num',
                                                   Pipeline(steps=[('imputer',
                                                                    SimpleImputer()),
                                                                   ('scaler',
                                                                    RobustScaler())]),
                                                   ['usd_goal_corrected',
                                                    'duration', 'len_name']),
                                                  ('cat',
                                                   Pipeline(steps=[('onehot',
                                                                    OneHotEncoder(handle_unknown='ignore'))]),
                                                   ['country', 'cat_full',
                                                    'month', 'name_non_chars',
                                                    'name_has_symbol',
                                                    'name_upper'])])),
                 ('lgbmclassifier', LGBMClassifier())]),
 'columntransformer__n_jobs': None,
 'columntransformer__remainder': 'passthrough',
 'columntransformer__sparse_threshold': 0.3,
 'columntransformer__transformer_weights': None,
 'columntransformer__transformers': [('num',
   Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]),
   ['usd_goal_corrected', 'duration', 'len_name']),
  ('cat',
   Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]),
   ['country',
    'cat_full',
    'month',
    'name_non_chars',
    'name_has_symbol',
    'name_upper'])],
 'columntransformer__verbose': False,
 'columntransformer__num': Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]),
 'columntransformer__cat': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]),
 'columntransformer__num__memory': None,
 'columntransformer__num__steps': [('imputer', SimpleImputer()),
  ('scaler', RobustScaler())],
 'columntransformer__num__verbose': False,
 'columntransformer__num__imputer': SimpleImputer(),
 'columntransformer__num__scaler': RobustScaler(),
 'columntransformer__num__imputer__add_indicator': False,
 'columntransformer__num__imputer__copy': True,
 'columntransformer__num__imputer__fill_value': None,
 'columntransformer__num__imputer__missing_values': nan,
 'columntransformer__num__imputer__strategy': 'mean',
 'columntransformer__num__imputer__verbose': 0,
 'columntransformer__num__scaler__copy': True,
 'columntransformer__num__scaler__quantile_range': (25.0, 75.0),
 'columntransformer__num__scaler__with_centering': True,
 'columntransformer__num__scaler__with_scaling': True,
 'columntransformer__cat__memory': None,
 'columntransformer__cat__steps': [('onehot',
   OneHotEncoder(handle_unknown='ignore'))],
 'columntransformer__cat__verbose': False,
 'columntransformer__cat__onehot': OneHotEncoder(handle_unknown='ignore'),
 'columntransformer__cat__onehot__categories': 'auto',
 'columntransformer__cat__onehot__drop': None,
 'columntransformer__cat__onehot__dtype': numpy.float64,
 'columntransformer__cat__onehot__handle_unknown': 'ignore',
 'columntransformer__cat__onehot__sparse': True,
 'pipeline__memory': None,
 'pipeline__steps': [('columntransformer',
   ColumnTransformer(remainder='passthrough',
                     transformers=[('num',
                                    Pipeline(steps=[('imputer', SimpleImputer()),
                                                    ('scaler', RobustScaler())]),
                                    ['usd_goal_corrected', 'duration',
                                     'len_name']),
                                   ('cat',
                                    Pipeline(steps=[('onehot',
                                                     OneHotEncoder(handle_unknown='ignore'))]),
                                    ['country', 'cat_full', 'month',
                                     'name_non_chars', 'name_has_symbol',
                                     'name_upper'])])),
  ('lgbmclassifier', LGBMClassifier())],
 'pipeline__verbose': False,
 'pipeline__columntransformer': ColumnTransformer(remainder='passthrough',
                   transformers=[('num',
                                  Pipeline(steps=[('imputer', SimpleImputer()),
                                                  ('scaler', RobustScaler())]),
                                  ['usd_goal_corrected', 'duration',
                                   'len_name']),
                                 ('cat',
                                  Pipeline(steps=[('onehot',
                                                   OneHotEncoder(handle_unknown='ignore'))]),
                                  ['country', 'cat_full', 'month',
                                   'name_non_chars', 'name_has_symbol',
                                   'name_upper'])]),
 'pipeline__lgbmclassifier': LGBMClassifier(),
 'pipeline__columntransformer__n_jobs': None,
 'pipeline__columntransformer__remainder': 'passthrough',
 'pipeline__columntransformer__sparse_threshold': 0.3,
 'pipeline__columntransformer__transformer_weights': None,
 'pipeline__columntransformer__transformers': [('num',
   Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]),
   ['usd_goal_corrected', 'duration', 'len_name']),
  ('cat',
   Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]),
   ['country',
    'cat_full',
    'month',
    'name_non_chars',
    'name_has_symbol',
    'name_upper'])],
 'pipeline__columntransformer__verbose': False,
 'pipeline__columntransformer__num': Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]),
 'pipeline__columntransformer__cat': Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]),
 'pipeline__columntransformer__num__memory': None,
 'pipeline__columntransformer__num__steps': [('imputer', SimpleImputer()),
  ('scaler', RobustScaler())],
 'pipeline__columntransformer__num__verbose': False,
 'pipeline__columntransformer__num__imputer': SimpleImputer(),
 'pipeline__columntransformer__num__scaler': RobustScaler(),
 'pipeline__columntransformer__num__imputer__add_indicator': False,
 'pipeline__columntransformer__num__imputer__copy': True,
 'pipeline__columntransformer__num__imputer__fill_value': None,
 'pipeline__columntransformer__num__imputer__missing_values': nan,
 'pipeline__columntransformer__num__imputer__strategy': 'mean',
 'pipeline__columntransformer__num__imputer__verbose': 0,
 'pipeline__columntransformer__num__scaler__copy': True,
 'pipeline__columntransformer__num__scaler__quantile_range': (25.0, 75.0),
 'pipeline__columntransformer__num__scaler__with_centering': True,
 'pipeline__columntransformer__num__scaler__with_scaling': True,
 'pipeline__columntransformer__cat__memory': None,
 'pipeline__columntransformer__cat__steps': [('onehot',
   OneHotEncoder(handle_unknown='ignore'))],
 'pipeline__columntransformer__cat__verbose': False,
 'pipeline__columntransformer__cat__onehot': OneHotEncoder(handle_unknown='ignore'),
 'pipeline__columntransformer__cat__onehot__categories': 'auto',
 'pipeline__columntransformer__cat__onehot__drop': None,
 'pipeline__columntransformer__cat__onehot__dtype': numpy.float64,
 'pipeline__columntransformer__cat__onehot__handle_unknown': 'ignore',
 'pipeline__columntransformer__cat__onehot__sparse': True,
 'pipeline__lgbmclassifier__boosting_type': 'gbdt',
 'pipeline__lgbmclassifier__class_weight': None,
 'pipeline__lgbmclassifier__colsample_bytree': 1.0,
 'pipeline__lgbmclassifier__importance_type': 'split',
 'pipeline__lgbmclassifier__learning_rate': 0.1,
 'pipeline__lgbmclassifier__max_depth': -1,
 'pipeline__lgbmclassifier__min_child_samples': 20,
 'pipeline__lgbmclassifier__min_child_weight': 0.001,
 'pipeline__lgbmclassifier__min_split_gain': 0.0,
 'pipeline__lgbmclassifier__n_estimators': 100,
 'pipeline__lgbmclassifier__n_jobs': -1,
 'pipeline__lgbmclassifier__num_leaves': 31,
 'pipeline__lgbmclassifier__objective': None,
 'pipeline__lgbmclassifier__random_state': None,
 'pipeline__lgbmclassifier__reg_alpha': 0.0,
 'pipeline__lgbmclassifier__reg_lambda': 0.0,
 'pipeline__lgbmclassifier__silent': True,
 'pipeline__lgbmclassifier__subsample': 1.0,
 'pipeline__lgbmclassifier__subsample_for_bin': 200000,
 'pipeline__lgbmclassifier__subsample_freq': 0}


clf = make_pipeline(
    preprocessing,
    LGBMClassifier(learning_rate=0.7, boosting_type="gbdt", objective='binary', metric='accuracy', max_depth=-1)
)

clf.fit(x_train, y_train)
    
predict_plot(x_train, y_train, clf, 'GBM')

2021/06/17 06:47:11 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID 'c45ead5f7eee4144b07aa0af4064a539', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current sklearn workflow
2021/06/17 06:47:11 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('columntransformer', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Ro...`
2021/06/17 06:47:11 WARNING mlflow.utils: Truncated the value of the key `columntransformer`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:47:11 WARNING mlflow.utils: Truncated the value of the key `columntransformer__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:47:16 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."


predict_plot(x_test, y_test, clf, 'GBM')


from sklearn.base import BaseEstimator
class ClfSwitcher(BaseEstimator):

    def __init__(
        self, 
        estimator = LogisticRegression(),
    ):
        """
        A Custom BaseEstimator that can switch between classifiers.
        :param estimator: sklearn object - The classifier
        """ 

        self.estimator = estimator


    def fit(self, X, y=None, **kwargs):
        self.estimator.fit(X, y)
        return self


    def predict(self, X, y=None):
        return self.estimator.predict(X)


    def predict_proba(self, X):
        return self.estimator.predict_proba(X)


    def score(self, X, y):
        return self.estimator.score(X, y)


pipeline = Pipeline([('preprocessing', preprocessing), ('clf', ClfSwitcher())])

parameters = [
    {
        'clf__estimator': [LogisticRegression()],
        'clf__estimator__solver': ["lbfgs", "liblinear"],
        "clf__estimator__penalty": ["l2"],
        "clf__estimator__C": [0.1, 0.2, 0.3, 0.5, 1.0],
        "clf__estimator__max_iter": [100, 1000, 2000],
    },
    {
        'clf__estimator': [LogisticRegression()],
        'clf__estimator__solver': ["liblinear"],
        "clf__estimator__penalty": ["l1"],
        "clf__estimator__C": [0.1, 0.2, 0.3, 0.5, 1.0],
        "clf__estimator__max_iter": [100, 1000, 2000],
    },
]

gscv = GridSearchCV(pipeline, parameters, cv=2, n_jobs=-1, verbose=3)
gs_model = gscv.fit(x_train, y_train)

print(gs_model.best_params_, gs_model.best_score_)

2021/06/17 06:47:26 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '6faf78cd177345cfb00eb2961575bb43', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current sklearn workflow
2021/06/17 06:47:26 WARNING mlflow.utils: Truncated the value of the key `estimator`. Truncated value: `Pipeline(steps=[('preprocessing',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('num',
                                                  Pipeline(steps=[('imputer',
                  ...`
2021/06/17 06:47:26 WARNING mlflow.utils: Truncated the value of the key `param_grid`. Truncated value: `[{'clf__estimator': [LogisticRegression()], 'clf__estimator__solver': ['lbfgs', 'liblinear'], 'clf__estimator__penalty': ['l2'], 'clf__estimator__C': [0.1, 0.2, 0.3, 0.5, 1.0], 'clf__estimator__max_iter': [100, 1000, 2000]}, {'clf__estimator': [Lo...`
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.

Fitting 2 folds for each of 45 candidates, totalling 90 fits

[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    7.4s
[Parallel(n_jobs=-1)]: Done  90 out of  90 | elapsed:  2.8min finished
C:\Users\ymaricar\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:762: ConvergenceWarning:

lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression

2021/06/17 06:50:17 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:18 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:19 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:20 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:21 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:22 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:23 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
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                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
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                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
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                                                 ('scaler', Robust...`
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                  transformers=[('num',
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                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
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                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:24 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
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                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
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                  transformers=[('num',
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                                                 ('scaler', Robust...`
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                  transformers=[('num',
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                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `steps`. Truncated value: `[('preprocessing', ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', Robust...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `preprocessing`. Truncated value: `ColumnTransformer(remainder='passthrough',
                  transformers=[('num',
                                 Pipeline(steps=[('imputer', SimpleImputer()),
                                                 ('scaler', RobustScaler())]),
      ...`
2021/06/17 06:50:25 WARNING mlflow.utils: Truncated the value of the key `preprocessing__transformers`. Truncated value: `[('num', Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', RobustScaler())]), ['usd_goal_corrected', 'duration', 'len_name']), ('cat', Pipeline(steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))]), ['country', 'cat_full', 'month', 'n...`

{'clf__estimator': LogisticRegression(C=0.3), 'clf__estimator__C': 0.3, 'clf__estimator__max_iter': 100, 'clf__estimator__penalty': 'l2', 'clf__estimator__solver': 'lbfgs'} 0.6767994140970223


# x = pd.get_dummies(x, columns = ['month','category','main_category','country'])
x = pd.get_dummies(x, columns = ['month','cat_full','country'])


from sklearn.preprocessing import RobustScaler
num_cols = ['usd_goal_corrected', 'duration', 'len_name']

transformer = RobustScaler().fit(x[num_cols])


x[num_cols] = transformer.transform(x[num_cols])


x.describe()


from sklearn.model_selection import train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=SEED)


print('x_train.shape:', x_train.shape)
print('y_train.shape:', y_train.shape)
print('x_test.shape :', x_test.shape)
print('y_test.shape :', y_test.shape)

x_train.shape: (225309, 204)
y_train.shape: (225309,)
x_test.shape : (56328, 204)
y_test.shape : (56328,)


# Creating the model:
lr = LogisticRegression(solver='liblinear') 

# Training the model with the training datas:
lr.fit(x_train, y_train)

2021/06/17 06:50:28 INFO mlflow.utils.autologging_utils: Created MLflow autologging run with ID '9ea0091684414f31985e5d317df363b0', which will track hyperparameters, performance metrics, model artifacts, and lineage information for the current sklearn workflow
2021/06/17 06:50:33 WARNING mlflow.utils.autologging_utils: MLflow autologging encountered a warning: "C:\Users\ymaricar\anaconda3\lib\site-packages\mlflow\models\signature.py:127: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values <https://www.mlflow.org/docs/latest/models.html#handling-integers-with-missing-values>`_ for more details."

LogisticRegression(solver='liblinear')


y_pred_lr = lr.predict(x_test)

# test data set auc error
print('Train data ROC/AUC :', roc_auc_score(y_true=y_train, y_score=lr.predict(x_train)))
print('Test data ROC/AUC :', roc_auc_score(y_true=y_test, y_score=y_pred_lr))

# confusion matrix
print('\nConfusion matrix')
print(confusion_matrix(y_true=y_test, y_pred=y_pred_lr))

# classification matrix
print('\nClassification matrix')
print(classification_report(y_true=y_test, y_pred=y_pred_lr))

Train data ROC/AUC : 0.6468634521568741
Test data ROC/AUC : 0.6458125755654817

Confusion matrix
[[27079  6649]
 [11554 11046]]

Classification matrix
              precision    recall  f1-score   support

           0       0.70      0.80      0.75     33728
           1       0.62      0.49      0.55     22600

    accuracy                           0.68     56328
   macro avg       0.66      0.65      0.65     56328
weighted avg       0.67      0.68      0.67     56328


%%time

from sklearn.model_selection import GridSearchCV

grid = {'C': np.logspace(-3,3,7), 'penalty': ['l1', 'l2']}

# Creating the model:
lr = LogisticRegression(solver='liblinear') 

# Creating GridSearchCV model:
lr_cv = GridSearchCV(lr, grid, cv=10, scoring='roc_auc') # Using lr model, grid parameters and cross validation of 10 (10 times of accuracy calculation will be applied) 

# Training the model:
lr_cv.fit(x_train, y_train)

print('best paremeters for logistic regression with liblinear: ', lr_cv.best_params_)
print('best score for logistic regression after grid search cv:', lr_cv.best_score_)

best paremeters for logistic regression with liblinear:  {'C': 1.0, 'penalty': 'l2'}
best score for logistic regression after grid search cv: 0.7278458440411556
Wall time: 15min 24s


lr_tuned = LogisticRegression(solver='liblinear', C=1.0, penalty='l2')
lr_tuned.fit(x_train, y_train)

y_pred_lr = lr_tuned.predict(x_test)

# test data set auc error
print('Train data ROC/AUC :', roc_auc_score(y_true=y_train, y_score=lr_tuned.predict(x_train)))
print('Test data ROC/AUC :', roc_auc_score(y_true=y_test, y_score=y_pred_lr))

# confusion matrix
print('\nConfusion matrix')
print(confusion_matrix(y_true=y_test, y_pred=y_pred_lr))

# classification matrix
print('\nClassification matrix')
print(classification_report(y_true=y_test, y_pred=y_pred_lr))

Train data ROC/AUC : 0.6468634521568741
Test data ROC/AUC : 0.6458125755654817

Confusion matrix
[[27079  6649]
 [11554 11046]]

Classification matrix
              precision    recall  f1-score   support

           0       0.70      0.80      0.75     33728
           1       0.62      0.49      0.55     22600

    accuracy                           0.68     56328
   macro avg       0.66      0.65      0.65     56328
weighted avg       0.67      0.68      0.67     56328


from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score

# Compute fpr, tpr, thresholds and roc auc
fpr, tpr, thresholds = roc_curve(y_test, y_pred_lr)
roc_auc = roc_auc_score(y_test, y_pred_lr)

# Plot ROC curve
plt.plot(fpr, tpr, label='ROC curve (area = %0.3f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')  # random predictions curve
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate or (1 - Specifity)')
plt.ylabel('True Positive Rate or (Sensitivity)')
plt.title('Receiver Operating Characteristic')
plt.legend(loc="lower right")

<matplotlib.legend.Legend at 0x2a6c754cf70>


# get importance
importance = lr_tuned.coef_
# summarize feature importance
sort_list = [(x.columns[i], v) for i,v in enumerate(importance[0])]

sort_list.sort(key=lambda x:x[1])


# plot feature importance
plt.bar([i for i in range(len(sort_list))], [i[1] for i in sort_list])
plt.show()

for i in sort_list:
    print('%s: %.5f' % (i[0],i[1]))

cat_full_Technology-Web: -1.71453
cat_full_Technology-Apps: -1.66991
cat_full_Games-Mobile Games: -1.46526
cat_full_Journalism-Video: -1.39119
cat_full_Crafts-Candles: -1.33941
cat_full_Food-Bacon: -1.33810
cat_full_Crafts-DIY: -1.22958
cat_full_Crafts-Embroidery: -1.17718
cat_full_Crafts-Crochet: -1.17300
cat_full_Music-Hip-Hop: -1.16235
cat_full_Photography-Nature: -1.10124
cat_full_Journalism-Photo: -1.05694
cat_full_Photography-Places: -1.05424
cat_full_Food-Farmer's Markets: -1.05318
cat_full_Food-Events: -1.02211
cat_full_Technology-Software: -0.93443
cat_full_Journalism-Web: -0.93288
cat_full_Food-Food Trucks: -0.92030
cat_full_Film & Video-Action: -0.88058
cat_full_Fashion-Jewelry: -0.83463
cat_full_Fashion-Childrenswear: -0.78627
cat_full_Fashion-Couture: -0.77840
cat_full_Publishing-Young Adult: -0.77808
cat_full_Fashion-Ready-to-wear: -0.76492
cat_full_Photography-People: -0.74823
cat_full_Music-R&B: -0.74178
cat_full_Fashion-Apparel: -0.73162
cat_full_Crafts-Woodworking: -0.71396
cat_full_Crafts-Crafts: -0.70795
cat_full_Film & Video-Television: -0.69989
cat_full_Games-Live Games: -0.62705
usd_goal_corrected: -0.60201
country_IT: -0.59630
cat_full_Crafts-Quilts: -0.59579
cat_full_Publishing-Academic: -0.59180
cat_full_Publishing-Fiction: -0.57108
cat_full_Design-Interactive Design: -0.56981
cat_full_Photography-Animals: -0.55697
cat_full_Crafts-Weaving: -0.54599
cat_full_Crafts-Glass: -0.54051
cat_full_Art-Digital Art: -0.53963
cat_full_Crafts-Printing: -0.53699
cat_full_Food-Restaurants: -0.53504
cat_full_Journalism-Audio: -0.51288
cat_full_Food-Community Gardens: -0.50704
cat_full_Art-Video Art: -0.46109
cat_full_Film & Video-Family: -0.45549
cat_full_Journalism-Print: -0.44977
cat_full_Technology-Flight: -0.42039
cat_full_Fashion-Fashion: -0.40441
cat_full_Food-Farms: -0.40236
cat_full_Food-Cookbooks: -0.39804
cat_full_Film & Video-Experimental: -0.39282
cat_full_Publishing-Nonfiction: -0.37255
country_AT: -0.36524
cat_full_Food-Small Batch: -0.36433
cat_full_Technology-Technology: -0.35116
cat_full_Art-Textiles: -0.33499
country_NL: -0.30660
cat_full_Journalism-Journalism: -0.30573
country_NO: -0.30400
country_ES: -0.29748
cat_full_Crafts-Stationery: -0.27852
cat_full_Games-Games: -0.26604
month_7: -0.26586
cat_full_Games-Video Games: -0.26111
cat_full_Publishing-Translations: -0.25421
cat_full_Food-Drinks: -0.25133
cat_full_Publishing-Poetry: -0.23221
cat_full_Art-Painting: -0.22980
cat_full_Publishing-Publishing: -0.22140
cat_full_Fashion-Pet Fashion: -0.20958
cat_full_Comics-Events: -0.20605
cat_full_Music-Electronic Music: -0.19065
cat_full_Dance-Workshops: -0.18918
cat_full_Design-Architecture: -0.17246
month_8: -0.16879
country_DE: -0.16389
country_BE: -0.16039
cat_full_Games-Gaming Hardware: -0.15757
cat_full_Photography-Photography: -0.15558
cat_full_Art-Mixed Media: -0.15548
cat_full_Food-Food: -0.15423
month_1: -0.14945
cat_full_Design-Graphic Design: -0.14624
month_12: -0.13319
name_non_chars: -0.12255
cat_full_Art-Conceptual Art: -0.10551
country_HK: -0.10249
duration: -0.09232
cat_full_Film & Video-Webseries: -0.08261
country_IE: -0.07953
country_AU: -0.07619
country_CH: -0.07051
cat_full_Publishing-Children's Books: -0.06712
country_MX: -0.05275
cat_full_Fashion-Accessories: -0.04693
cat_full_Photography-Fine Art: -0.04689
cat_full_Film & Video-Music Videos: -0.04669
month_6: -0.04390
cat_full_Film & Video-Thrillers: -0.03300
cat_full_Film & Video-Animation: -0.03201
month_9: -0.01836
cat_full_Crafts-Taxidermy: -0.01616
month_11: -0.00674
month_5: -0.00063
country_CA: 0.00061
month_10: 0.00802
month_2: 0.00972
cat_full_Technology-Fabrication Tools: 0.00979
country_SE: 0.01120
month_4: 0.02061
month_3: 0.02700
cat_full_Film & Video-Horror: 0.04823
cat_full_Film & Video-Film & Video: 0.07188
country_NZ: 0.07627
cat_full_Film & Video-Movie Theaters: 0.08053
name_has_symbol: 0.08074
cat_full_Music-Metal: 0.08364
cat_full_Music-Latin: 0.09738
cat_full_Design-Design: 0.12884
cat_full_Film & Video-Romance: 0.13410
cat_full_Fashion-Footwear: 0.13517
cat_full_Food-Vegan: 0.14005
cat_full_Art-Art: 0.14914
cat_full_Food-Spaces: 0.15762
cat_full_Dance-Spaces: 0.16240
cat_full_Film & Video-Fantasy: 0.16297
country_DK: 0.16648
cat_full_Art-Sculpture: 0.20047
name_upper: 0.20463
country_GB: 0.20984
cat_full_Publishing-Calendars: 0.21214
cat_full_Music-Punk: 0.21696
cat_full_Games-Puzzles: 0.22328
cat_full_Art-Performance Art: 0.23715
cat_full_Technology-Gadgets: 0.24128
cat_full_Publishing-Radio & Podcasts: 0.24825
cat_full_Art-Ceramics: 0.25001
cat_full_Film & Video-Comedy: 0.26558
country_FR: 0.28360
cat_full_Technology-Space Exploration: 0.28785
cat_full_Technology-Makerspaces: 0.30825
cat_full_Art-Illustration: 0.30903
cat_full_Publishing-Zines: 0.31109
cat_full_Photography-Photobooks: 0.33442
country_SG: 0.34003
cat_full_Design-Product Design: 0.35438
cat_full_Music-Faith: 0.35736
cat_full_Publishing-Periodicals: 0.37631
country_US: 0.37812
country_LU: 0.38768
len_name: 0.39323
cat_full_Music-Blues: 0.39781
cat_full_Publishing-Literary Journals: 0.41546
cat_full_Crafts-Knitting: 0.41906
cat_full_Technology-Hardware: 0.41955
cat_full_Film & Video-Documentary: 0.42267
cat_full_Film & Video-Drama: 0.43496
cat_full_Games-Playing Cards: 0.43620
cat_full_Design-Civic Design: 0.43744
cat_full_Music-World Music: 0.44756
cat_full_Music-Pop: 0.48144
cat_full_Technology-3D Printing: 0.50419
cat_full_Film & Video-Festivals: 0.50880
cat_full_Technology-Robots: 0.51376
cat_full_Music-Music: 0.51571
cat_full_Technology-Wearables: 0.53117
cat_full_Film & Video-Narrative Film: 0.54874
cat_full_Technology-DIY Electronics: 0.55686
cat_full_Film & Video-Science Fiction: 0.58022
cat_full_Music-Rock: 0.58286
cat_full_Comics-Comics: 0.63514
cat_full_Music-Kids: 0.66428
cat_full_Art-Public Art: 0.66524
cat_full_Art-Installations: 0.66662
cat_full_Publishing-Art Books: 0.68980
cat_full_Technology-Sound: 0.73211
cat_full_Crafts-Pottery: 0.76961
cat_full_Theater-Musical: 0.79173
cat_full_Theater-Experimental: 0.84105
cat_full_Film & Video-Shorts: 0.86846
cat_full_Comics-Graphic Novels: 0.87065
cat_full_Publishing-Anthologies: 0.87343
cat_full_Theater-Immersive: 0.88126
cat_full_Theater-Plays: 0.91361
cat_full_Comics-Webcomics: 0.94256
cat_full_Comics-Comic Books: 0.94655
cat_full_Theater-Spaces: 0.96437
cat_full_Theater-Festivals: 0.99423
cat_full_Technology-Camera Equipment: 1.00353
cat_full_Dance-Performances: 1.00502
cat_full_Music-Jazz: 1.03557
cat_full_Theater-Theater: 1.04867
cat_full_Crafts-Letterpress: 1.08059
cat_full_Music-Country & Folk: 1.09114
cat_full_Music-Indie Rock: 1.09831
cat_full_Music-Classical Music: 1.13767
cat_full_Music-Chiptune: 1.21048
cat_full_Dance-Dance: 1.21385
cat_full_Games-Tabletop Games: 1.22086
cat_full_Design-Typography: 1.23384
cat_full_Dance-Residencies: 1.36250
cat_full_Comics-Anthologies: 1.83013


print("The most promising categories to start a kickstarter in are:",", ".join(list(more_success_than_failed.keys())))

The most promising categories to start a kickstarter in are: Music-Music, Film & Video-Shorts, Games-Tabletop Games, Music-Rock, Theater-Theater, Comics-Comics, Music-Indie Rock, Music-Pop, Music-Country & Folk, Art-Public Art, Art-Illustration, Publishing-Art Books, Music-Classical Music, Comics-Comic Books, Dance-Dance, Music-Jazz, Comics-Graphic Novels, Theater-Plays, Theater-Musical, Dance-Performances, Technology-DIY Electronics, Comics-Webcomics, Art-Installations, Theater-Festivals, Technology-Camera Equipment, Theater-Experimental, Music-Punk, Theater-Immersive, Film & Video-Festivals, Publishing-Literary Journals, Publishing-Anthologies, Theater-Spaces, Crafts-Knitting, Comics-Anthologies, Crafts-Pottery, Design-Typography, Dance-Residencies, Crafts-Letterpress, Music-Chiptune

	ID	name	category	main_category	currency	deadline	launched	state	backers	country	real_usd_pledged	usd_goal
count	2.818530e+05	281853	281853	281853	281853	281853	281853	281853	281853.000000	281643	2.818530e+05	2.818530e+05
unique	NaN	280147	158	15	13	257784	281499	2	NaN	21	NaN	NaN
top	NaN	New EP/Music Development	Product Design	Film & Video	USD	2012-01-01 05:59:00	2015-05-26 18:00:38	failed	NaN	US	NaN	NaN
freq	NaN	15	14540	51130	229975	46	2	168605	NaN	229824	NaN	NaN
mean	1.074956e+09	NaN	NaN	NaN	NaN	NaN	NaN	NaN	111.852530	NaN	9.293935e+03	4.115817e+04
std	6.194665e+08	NaN	NaN	NaN	NaN	NaN	NaN	NaN	996.270201	NaN	9.314645e+04	1.082761e+06
min	5.971000e+03	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000000	NaN	0.000000e+00	1.000000e-02
25%	5.373183e+08	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2.000000	NaN	5.000000e+01	2.000000e+03
50%	1.075897e+09	NaN	NaN	NaN	NaN	NaN	NaN	NaN	15.000000	NaN	7.914802e+02	5.000000e+03
75%	1.611414e+09	NaN	NaN	NaN	NaN	NaN	NaN	NaN	62.000000	NaN	4.500000e+03	1.500000e+04
max	2.147476e+09	NaN	NaN	NaN	NaN	NaN	NaN	NaN	219382.000000	NaN	2.033899e+07	1.663627e+08

	ID	name	category	main_category	currency	deadline	launched	state	country	real_usd_pledged	usd_goal
1833	1012744036	An Oratorio for our Time - Last Stop Cafe	Music	Music	USD	2016-05-26 19:46:26	2016-04-26 19:46:26	successful	NaN	5170.00	5000.0
3670	1024989802	Jackson Ruby: The Cassette Album	Music	Music	USD	2016-05-11 19:29:17	2016-04-11 19:29:17	successful	NaN	5296.00	5000.0
4004	1027275369	Help Parker Brown make his first solo album	Music	Music	USD	2016-05-21 16:14:28	2016-04-21 16:14:28	successful	NaN	5077.00	3800.0
4180	1028691308	Help BETHANY record a NEW single in Nash!	Music	Music	USD	2016-05-08 22:37:00	2016-04-04 23:32:00	successful	NaN	3502.13	3500.0
6157	1041708793	Serena Gabriel's first CD!!!!!!: Diving Deep	Music	Music	USD	2016-05-01 04:22:00	2016-03-25 17:20:21	successful	NaN	3787.00	3500.0

	ID	name	category	main_category	currency	deadline	launched	state	backers	country	real_usd_pledged	usd_goal
1529	1010584633	"A Fresh Start"	Shorts	Film & Video	USD	2011-09-25 06:00:00	2011-08-28 21:27:52	successful	24	US	3000.0	3000.0
240126	713417995	"A Fresh Start"	Documentary	Film & Video	USD	2013-03-27 02:51:47	2013-01-26 03:51:47	failed	26	US	1417.0	5000.0
96268	1656736114	"ONE"	Apps	Technology	USD	2016-02-25 17:37:04	2016-01-26 17:37:04	failed	0	US	0.0	10000.0
86497	159049492	"ONE"	Classical Music	Music	USD	2016-09-26 03:19:12	2016-08-12 03:19:12	successful	113	US	10261.0	10000.0
281045	996180421	"On The Road"	Webseries	Film & Video	USD	2015-01-22 17:31:06	2014-12-23 17:31:06	failed	0	US	0.0	80000.0

	state	country	usd_goal	duration	month	len_name	name_non_chars	name_has_symbol	name_upper	cat_full	usd_goal_corrected
count	281637.000000	281637	2.816370e+05	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637	281637.000000
unique	NaN	21	NaN	NaN	NaN	NaN	NaN	NaN	NaN	165	NaN
top	NaN	US	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Design-Product Design	NaN
freq	NaN	229818	NaN	NaN	NaN	NaN	NaN	NaN	NaN	14538	NaN
mean	0.401733	NaN	4.117401e+04	33.976253	6.372977	34.023541	0.963318	0.105174	0.207533	NaN	8.587772
std	0.490249	NaN	1.083168e+06	12.893991	3.274156	15.917254	0.187980	0.306779	0.405541	NaN	1.659265
min	0.000000	NaN	1.000000e-02	1.000000	1.000000	1.000000	0.000000	0.000000	0.000000	NaN	0.009950
25%	0.000000	NaN	2.000000e+03	30.000000	4.000000	20.000000	1.000000	0.000000	0.000000	NaN	7.601402
50%	0.000000	NaN	5.000000e+03	30.000000	6.000000	33.000000	1.000000	0.000000	0.000000	NaN	8.517393
75%	1.000000	NaN	1.500000e+04	36.000000	9.000000	48.000000	1.000000	0.000000	0.000000	NaN	9.615872
max	1.000000	NaN	1.663627e+08	92.000000	12.000000	85.000000	1.000000	1.000000	1.000000	NaN	18.929681

	duration	len_name	name_non_chars	name_has_symbol	name_upper	usd_goal_corrected	month_1	month_2	month_3	month_4	...	country_IE	country_IT	country_LU	country_MX	country_NL	country_NO	country_NZ	country_SE	country_SG	country_US
count	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	...	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000	281637.000000
mean	0.662709	0.036555	0.963318	0.105174	0.207533	0.034937	0.072846	0.080618	0.090038	0.087354	...	0.001679	0.004950	0.000117	0.000014	0.006650	0.001527	0.003526	0.003785	0.000249	0.816008
std	2.148998	0.568473	0.187980	0.306779	0.405541	0.823673	0.259883	0.272248	0.286237	0.282353	...	0.040947	0.070179	0.010824	0.003769	0.081279	0.039044	0.059274	0.061406	0.015763	0.387478
min	-4.833333	-1.142857	0.000000	0.000000	0.000000	-4.223167	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.000000	-0.464286	1.000000	0.000000	0.000000	-0.454706	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
50%	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
75%	1.000000	0.535714	1.000000	0.000000	0.000000	0.545294	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
max	10.333333	1.857143	1.000000	1.000000	1.000000	5.168749	1.000000	1.000000	1.000000	1.000000	...	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000

Kickstarter dataset project - Yasmine Maricar¶

Description¶

Reading and cleaning up the dataframe¶

I am omitting this step for the sake of clarity. This part would made us read from the original dataset and cleaning it up + adding relevant features if the original features are not right.¶

Going from the final dataframe¶

Distribution of goals and pledges¶

Feature engineering¶

I. Let's prepare the dataset to train the model¶

1. Generating html report with pandas profiling¶

2. Explore manually¶

II. Model training¶

Preprocessing¶

Without sklearn pipelines¶

Let's try cross-validation¶

Conclusion¶

	ID	name	category	main_category	currency	deadline	launched	state	backers	country	...	usd_goal	duration	month	year_month	len_name	name_nb_words	name_non_chars	name_has_symbol	cat_full
0	1000002330	The Songs of Adelaide & Abullah	Poetry	Publishing	GBP	2015-10-09 11:36:00	2015-08-11 12:12:28	failed	0	GB	...	1533.954368	58	8	2015-8	31	6	1	0	Publishing-Poetry
1	1000004038	Where is Hank?	Narrative Film	Film & Video	USD	2013-02-26 00:20:50	2013-01-12 00:20:50	failed	3	US	...	45000.000000	45	1	2013-1	14	3	1	1	Film & Video-Narrative Film
2	1000007540	ToshiCapital Rekordz Needs Help to Complete Album	Music	Music	USD	2012-04-16 04:24:11	2012-03-17 03:24:11	failed	1	US	...	5000.000000	30	3	2012-3	49	7	1	0	Music-Music
3	1000014025	Monarch Espresso Bar	Restaurants	Food	USD	2016-04-01 13:38:27	2016-02-26 13:38:27	successful	224	US	...	50000.000000	35	2	2016-2	20	3	1	0	Food-Restaurants
4	1000023410	Support Solar Roasted Coffee & Green Energy! ...	Food	Food	USD	2014-12-21 18:30:44	2014-12-01 18:30:44	successful	16	US	...	1000.000000	20	12	2014-12	60	9	1	1	Food-Food